2022
DOI: 10.1049/gtd2.12635
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A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations

Abstract: It is a long‐term goal in external insulation studies to determine the discharge voltages of complicated engineering gaps by simulation methods. Based on the one‐to‐one correspondence between air gap structure and the static electric field (EF) distribution, this paper characterizes the transmission tower gap configuration by spatial EF features, which were used for machine learning to achieve switching impulse discharge voltage prediction. An interelectrode path and a conical zone between the energized sub‐co… Show more

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